Blogs

Your engineers are already using Cursor, Copilot, Claude Code, and other AI tools. But if requirements, architecture, development, testing, review, and release still operate the same way, you haven't built an AI-native SDLC. You've built an AI-augmented one.

The word "agentic" gets attached to almost anything with a chat interface right now, so it's worth being precise about what it actually means before mapping it to the deal lifecycle. Three distinct levels get sold under the same word, and the difference between them is exactly where a vendor's claim tends to fall apart.

If you have a SaaS renewal coming up in the next couple of quarters, you've probably seen the SaaSpocalypse headlines and wondered whether they actually apply to you, or whether it's just noise for people who trade software stocks. It applies to you. The pricing model behind your renewal is the same one that's already cracking in public.

An operating partner building an AI roadmap for private equity portfolio companies sits down with a fixed budget and five portfolio companies on the table. One is a healthcare services roll-up with three years of bootstrapped acquisitions and no unified reporting. One is a payments platform with a clean data stack and a product team already asking for a fraud-detection model. One just closed, and the new CTO is still mapping what exists. The mandate from the fund is the same for all five: build the AI story for the next board meeting.

Agentic AI is not a smarter chatbot or a better rules engine. An agentic system receives a goal, breaks it into steps, takes actions across tools and systems to complete those steps, and surfaces results for human review. In a claims context, that means the system moves the claim forward autonomously until it hits a decision that requires human judgment.

When we started mapping where AI could genuinely change delivery outcomes, a pattern emerged fast. Every high-value intervention point.